Monitoring rainfall and drought trends using remote sensing-derived climate and vegetation indices in semi-arid eco-zone, Nigeria

 Corresponding author: Favour N. Eze Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution License 4.0. Monitoring rainfall and drought trends using remote sensing-derived climate and vegetation indices in semi-arid eco-zone, Nigeria Favour N. Eze 1, *, Samuel O. Akande 2, William M. Raik 3, Abdulramon Temitope Shehu 4, Blessing Adebayo Oyeleke 5 and Udochukwu Emmanuel Chukwumezie 6 1 Department of Remote Sensing and GIS, Federal University of Technology Akure, Nigeria 2 Centre for Space Research and Applications (CESRA), Federal University of Technology Akure, Nigeria. 3 Department of Remote Sensing and GIS, Federal University of Technology Akure, Nigeria. 4 Department of Water Resources and Environmental Engineering, Ahmadu Bello University, Zaria Nigeria. 5 Department of Computer Engineering, Ladoke Akintola, University of Technology, Ogbomoso Nigeria. 6 Department of Mass Communication, University of Nigeria, Nsukka Nigeria. World Journal of Advanced Research and Reviews, 2025, 28(01), 660-674 Publication history: Received on 19 August 2025; revised on 06 October 2025; accepted on 08 October 2025 Article DOI: https://doi.org/10.30574/wjarr.2025.28.1.3340 Abstract Along with flooding and wildfire, Drought is a major natural hazard that continues to ravage the environment in many parts of the world, causing widespread social and economic devastation as evidence of climate change. This study aimed to assess the spatiotemporal pattern of drought events across Sudano-Sahelian Ecological Zone of Nigeria. This analyzed the daily accumulated precipitation and temperature from seven (7) weather stations in the study. Although scientists have relied on data obtained from meteorological stations. However, meteorological data are mostly scarce, expensive and sparsely distributed, especially in developing countries. This research was conducted using (CHIRPS) datasets from 1987-2022 to calculate SPEI and the SVI derived from MODIS using the Google Earth Engine cloud platform. The spatial and temporal variation of vegetation conditions in the Sudano-Sahelian region of Nigeria were examined using SVI. The results showed mild drought events at some parts of the study area, but there were severe to extreme drought events at Maiduguri station experiencing extreme drought in 2012, severe drought in 2004, 2005 and 1990, and moderately dry conditions in most years. Extremely wet conditions in 2012 and very wet conditions in 1994, 1996, and 1989. Katsina station experienced drought extremes in 2022, 2010, and 2005 while in Kano station, extreme drought was notably recorded in 2004 and 2006 (SPEI > -2). The findings show that drought affects the vegetation health and agricultural activities in the study area (SVI -3.7). The result of the study is important for policy decisions, water resources management in the face of increasing drought risks in the semi-arid region of Nigeria providing evidencebased insights for the development of early warning systems and strategic communication frameworks to mitigate the socio-economic impacts of droughts in vulnerable communities. Keywords: Drought; Climate change; Rainfall; Temperature; Vegetation; Remote Sensing 1. Introduction Drought is one of the costliest and the most widespread natural disasters. It is however less understood because of its large spatial extent and the difficulty in determining its onset and cessation compared to flood. (1); (2). Previous studies have reported that there is an increase in the frequency and intensity of extreme weather events such as drought and flood due to the impact of climate change (3). (4) asserted that more than one billion people around the world have been affected by one form of drought or another in the last two decades with a devastating economic loss, especially the droughts of the 1970s and 1980s that ravaged the Sahelian region of West Africa. Drought emerges as compelling World Journal of Advanced Research and Reviews, 2025, 28(01), 660-674 661 evidence of climate change and a global challenge anticipated to escalate in frequency, duration, and intensity, as projected by Huang (5); (6). This encompasses various dimensions of drought events, including their frequency, severity, duration, and spatial coverage, as outlined by (7). This encompasses various dimensions of drought events, including their frequency, severity, duration, and spatial coverage, as outlined by (7). Drought is a recurring natural disaster (9); (10), caused by low rainfall over a prolonged period (8); (9). It is widely regarded as a creeping phenomenon because it accumulates slowly over a long period (11); (13). Rainfall is known as a major contributing factor to drought events, and the aftermath is manifested in the reduction of reservoir water level, decrease of soil moisture, declining river flow and groundwater levels leading to a substantial effect on agricultural production (14), water resources (15), ecosystem function (16) both local and global environment. Virtually all climatic zones experience drought regardless of the region (17), (47). However, the arid and semi-arid areas are prone to drought due to low rainfall and a high rate of evapotranspiration, and the impact varies from one region to another (18). This makes the prediction of the onset and cessation of drought challenging (19); (20). This is common around the Sahelian regions that practice rain-fed agriculture (21). Source: (Author’s work) Figure 1 Relationship between Meteorological, Agricultural, and Hydrological Drought The drought events that occur in the Sahel-Sudano region of northern Nigeria are known to be as a result of late onset of the rainy season and early cessation of rainfall whereby causing a reduction in the length of rainy season (22). The Sudano-Sahelian region is known for its source of cereals and animal protein and the region is characterized by low rainfall and severe drought conditions due to climate change (23). The usage of vegetation cover is an important drought indicator since vegetation cannot survive without water. The use of remotely sensed data and GIS applications has proved effective in detecting, mapping, assessing, monitoring, and giving timely information regarding drought event features such as onset, intensity, duration, and spatial extent (24), (48). The sociological impact of drought is often heightened by a gap between scientific data and actionable public knowledge. Satellite-based indices used to monitor the health of vegetation cover include the Standard Vegetation Index (SVI) (25), the Normalized Difference Vegetation Index (NDVI) (26), the Precipitation Condition Index (PCI), the Temperature Condition Index (TCI), and the Vegetation Condition Index (VCI). The variability of climate in Nigeria, particularly the World Journal of Advanced Research and Reviews, 2025, 28(01), 660-674 662 Sahel region are experiencing frequent drought events due to frequent evaporative demand (27). Additionally, drought in the region has caused forest mortality that affects Carbon dioxide (CO2) sequestration, impacts soil compaction, hinders agriculture, and degrades the aesthetics of the surrounding area. This research will explore the geographical and temporal effects of drought on vegetation health in Nigeria's Sudano-Sahelian ecological zone. Drought can be classified as agricultural, meteorological, hydrological, or socio-economic (28); (29). 2. Material and method 2.1. Study Area Figure 2 Sudano-Sahelian Ecological Zone (SSEZ) of Nigeria. Source: (Author’s work) 2.1.1. Location, size and extent Figure 1.1 depicts the Sudano-Sahelian Ecological Zone (SSEZ), which covers roughly one-third of Nigeria's land area. This region extends longitudinally from 4° E to 14° E and latitudinally from 10° N to 14° N, from the Sokoto lowlands to the northern half of the Hausa high plains and the Chad basin. Sokoto, Kano, Katsina, Maiduguri, Nguru, Potiskum, and Gusau are the seven meteorological stations selected for this area. This ecological zone is home to more than a quarter of Nigeria's population and sustains three-quarters of the country's cattle population, over 75% of its goats and sheep, and virtually all its donkeys, camels, and horses. The predominant crops cultivated in this region include cereals, cowpeas, groundnuts, and cotton, as highlighted by (30). 2.2. Data Description and Sources This study calculated the daily accumulated precipitation and temperature from daily data of 1987–2022 for seven (7) weather stations from Sudano-Sahelian ecological zone of Nigeria. The weather stations include Sokoto, Katsina, Gusau, Kano, Nguru, Potiskum and Maiduguri. The satellite-based precipitation product was obtained from CHIRPS product obtained from www.climateserv.servirglobal.net covering 1987 to 2022 while the temperature data were defined from the website of Power.larc.nasa.gov. World Journal of Advanced Research and Reviews, 2025, 28(01), 660-674 663 Table 1 Data and their sources S/N Data type resolution Source Date 1. MOD13Q1.006 250-1000m https://glovis.usgs.gov./app. 2000-2022 2. CHIRPS Precipitation Dataset 0.05° × 0.05° https//climateserv.servirglobal.net 1987-2022 3. Temperature data 1° × 1° https//Power.larc.nasa.gov. 1987-2022 Table 2 List of the meteorological stations in the study area S/N Name of Station Latitude (0N) Longitude (0E) 1 Nguru 12.87 10.45 2 Kano 11.96 8.47 3 Maiduguri 11.84 13.17 4 Gusau 12.19 6.68 5 Katsina 12.99 7.61 6 Sokoto 13.05 5.26 7 Potiskum 11.71 11.12 Source: (Adegun and Odunuga, 2022) 2.3. Variability of Meteorological Drought 2.3.1. The Standardized Precipitation Evapotranspiration Index (SPEI) This study used the Standardized Precipitation Evapotranspiration Index (SPEI) to determine the duration and severity of drought occurrences in the study area. SPEI uses both precipitation and potential evapotranspiration (PET) data, accounting for the influence of air temperature, as opposed to the Standardized Precipitation Index (SPI), which only uses monthly precipitation data (35). SPEI can be calculated at any timescale, such as 1-, 3-, 6-, 12and 24months respectively (36). But in this study, six months’ timescale was used to calculate the SPEI. The SPEI values above zero signify moisture conditions that are higher than average, whereas values below zero indicate drier conditions. SPEI was calculated using the SPEI calculator application. This software was run from the Windows prompt, with input from a data file including a monthly time series of precipitation (mm), mean temperature (°C), and geographic coordinates of meteorological stations in the research area. The application calculates the SPEI accumulation using the 6-month timeframes and generates a new data file with the SPEI time series. SPEI data was analyzed to determine the intensity, duration, and severity of drought (37). Table 3 Classification of SPI and SPEI Values (Fung et al., 2020). Classification SPI (McKee et al., 1993) SPEI (Vicente-Serrano et al., 2010) Extremely wet dry ≥ 2.00 ≥ 2.00 Very wet 1.50 to 1.99 99 1.50 to 1.99 Moderately wet 1.00 to 1.49 1.00 to 1.49 Normal -0.99 to 0.99 −0.99 to 0.99 Moderately dry −1.49 to −1.00 −1.49 to −1.00 Extremely dry ≤ −2.00 ≤ −2.00 World Journal of Advanced Research and Reviews, 2025, 28(01), 660-674 664 2.4. Standardized Vegetation Index (SVI) Calculation The Standardized Vegetation Index (SVI) was developed by (25) and describes the probability of variation from the normal Enhanced Vegetation Index (EVI) over several years of data, on a weekly (or 16 days). The SVI can either be calculated through Enhanced Vegetation Index (EVI) or based on the Normalized Difference Vegetation Index (NDVI). Several studies have used the NDVI as an indicator for vegetation’s growth status and level of greenness; however, numerous studies have demonstrated that EVI outperforms NDVI in terms of vegetation coverage (38). More so, the EVI tends to boast the vegetation signals and enhance the monitoring of vegetation (39). This study used monthly EVI data from MOD13Q1 with a spatial resolution of 250m and a temporal resolution of 16 days. SVI was used to visualize vegetation changes for drought monitoring in the study area. This was calculated with the Google Earth Engine (GEE) code editor. EVI can separate soil and atmospheric influences from the plant signal by introducing a feedback term for simultaneous correction, and it has higher sensitivity under thick vegetation conditions and less influence from aerosols. The SVI is a z-score deviation from the mean in units of the standard deviation. When a Z-score is less than zero (0), it denotes an element less than the mean, when it is equal to zero (0), it is an element equal to the mean but when it is greater than zero (0), it means the element is greater than the mean. The Z-score shows how many standard deviations an item is from the mean; generally, the standard deviation indicates how dispersed the data set is (25). Table 4 The classification of SVI and drought levels (Juntakut et al., 2021) Level SVI Value SVI category Drought Drought category 1 1 1.50 to 2.50 Very high vegetation Very low drought 2 0.50 to 1.50 High vegetation Low drought 3 -0.50 to 0.50 Moderate vegetation Moderate drought 4 -1.50 to -0.50 Low vegetation High drought 5 -2.50 to -1.50 Very low vegetation Very high drought Source (Author) 3. Results and discussion 3.1. Meteorological Drought: Severity, Intensity, and Frequency Understanding drought characteristics is critical for developing adaptive solutions to alleviate drought-related harms. Drought severity is determined by the cumulative values of SPI or SPEI within a drought episode (Mckee et al., 1993), whereas drought frequency is determined by the total number of years of drought based on annual or seasonal indices (40). Figure 3(a-g) depicts meteorological drought patterns that provide a thorough understanding of the vulnerability of various meteorological stations to drought events in the study area. The results from the Gusau station (Figure 3a) show drought severity in 2004, 2022, and 1990, with significantly higher drought frequencies. Notable is the transition from mild drought in earlier years to more severe drought in later periods, with a sudden shift to an exceptionally wet period in 1991. This dynamic pattern agrees with findings from previous studies by (41) and (42), who similarly highlighted the variability in drought occurrences and the abrupt nature of climate shifts in the region. The rapid shift from extreme drought in 1990 to exceptionally wet conditions in 1991 in Gusau suggests a result of peculiarities of regional climatic changes and human activities such as deforestation (43). Kano's experience of extreme drought in 2004 and 2006, followed by a period of moderate drought between 2012 and 2022, agrees with the fluctuating nature of drought cycles. Nguru's extreme drought peaks in 1987 and 2004, along with an abrupt change of more frequent wet years in 2013, 1988, 1994, 1998, and 2020 because of climate change. Examining the meteorological conditions in Potiskum (Figure 3d) demonstrates a tendency towards moderately dry conditions with the highest drought recorded in 2022 (SPEI = -2). Conversely, 2020 marked the wettest year, exceeding SPEI 2.5. Most years displayed near-normal conditions, except for 1994, 2004, and 2013, exhibiting very wet indicators (SPEI > 1.5). This understanding aligns with assessments of Potiskum's climatic tendencies, emphasizing the importance of long-term drought risk assessments (28).Katsina (Figure 3e) faced drought extremes with a higher frequency in 2022, 2010, and 2005, to periodic severe drought events. Sokoto (Figure 3f) exhibited extreme and severe drought interspersed with moderately dry conditions which coincided with (44) recognition to recurrent drought with intermittent wet periods. In Figure 4.2g, Maiduguri experienced an extreme drought in 2012 and severe drought in World Journal of Advanced Research and Reviews, 2025, 28(01), 660-674 665 2004, 2005, and 1990, along with predominantly moderately dry conditions in most years. Extreme wetness in 1989 decreased to very wet conditions in 2012, followed by moderately wet conditions. Figure 3a-c Drought characteristics in (a) Gusau; (b) Kano; (c) Nguru (1987-2021) World Journal of Advanced Research and Reviews, 2025, 28(01), 660-674 666 Figure 3d-g Drought characteristics in (d) Potiskum; (e) Katsina; (f) Sokoto, and (g) Maiduguri (1987-2021) 3.2. The spatial distribution of vegetation conditions in the study area The application of the Standardized Vegetation Index (SVI) emerged as a pivotal tool in assessing and monitoring vegetation conditions in the Sudano-Sahelian zone of Nigeria during the years 2001, 2010, and 2020. The impact of drought on vegetation varies significantly across seasons and differs among regions as a result of precipitation stress. Figures 4-6 present a comprehensive spatio-temporal comparison of the vegetation conditions in the study area. World Journal of Advanced Research and Reviews, 2025, 28(01), 660-674 667 Figure 4 Standardized Vegetation Index at the Sudano-Sahelian Zone for (a) July, (b) August and (c) September, 2001. Source: (Author’s analysis) World Journal of Advanced Research and Reviews, 2025, 28(01), 660-674 668 Figure 5 Standardized Vegetation Index at the Sudano-Sahelian Zone for (a) July, (b) August and (c) September, 2010. Source: (Author’s analysis)